Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations15533
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 MiB
Average record size in memory578.9 B

Variable types

Numeric9
Categorical5
Boolean4

Alerts

Gender is highly overall correlated with Height and 2 other fieldsHigh correlation
Height is highly overall correlated with GenderHigh correlation
Weight is highly overall correlated with Gender and 2 other fieldsHigh correlation
WeightCategory is highly overall correlated with Gender and 2 other fieldsHigh correlation
family_history_with_overweight is highly overall correlated with Weight and 1 other fieldsHigh correlation
FAVC is highly imbalanced (57.4%)Imbalance
CAEC is highly imbalanced (61.2%)Imbalance
SMOKE is highly imbalanced (91.0%)Imbalance
SCC is highly imbalanced (79.0%)Imbalance
MTRANS is highly imbalanced (63.7%)Imbalance
id is uniformly distributedUniform
id has unique valuesUnique
FAF has 3799 (24.5%) zerosZeros
TUE has 4966 (32.0%) zerosZeros

Reproduction

Analysis started2025-10-23 09:47:01.464155
Analysis finished2025-10-23 09:47:15.004224
Duration13.54 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Uniform  Unique 

Distinct15533
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7766
Minimum0
Maximum15532
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T09:47:15.487460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile776.6
Q13883
median7766
Q311649
95-th percentile14755.4
Maximum15532
Range15532
Interquartile range (IQR)7766

Descriptive statistics

Standard deviation4484.1352
Coefficient of variation (CV)0.57740603
Kurtosis-1.2
Mean7766
Median Absolute Deviation (MAD)3883
Skewness0
Sum1.2062928 × 108
Variance20107468
MonotonicityStrictly increasing
2025-10-23T09:47:15.694186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
155321
 
< 0.1%
01
 
< 0.1%
11
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
Other values (15523)15523
99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
155321
< 0.1%
155311
< 0.1%
155301
< 0.1%
155291
< 0.1%
155281
< 0.1%
155271
< 0.1%
155261
< 0.1%
155251
< 0.1%
155241
< 0.1%
155231
< 0.1%

Gender
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size819.2 KiB
Male
7783 
Female
7750 

Length

Max length6
Median length4
Mean length4.9978755
Min length4

Characters and Unicode

Total characters77632
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male7783
50.1%
Female7750
49.9%

Length

2025-10-23T09:47:15.900029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T09:47:16.013065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male7783
50.1%
female7750
49.9%

Most occurring characters

ValueCountFrequency (%)
e23283
30.0%
a15533
20.0%
l15533
20.0%
M7783
 
10.0%
F7750
 
10.0%
m7750
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)77632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e23283
30.0%
a15533
20.0%
l15533
20.0%
M7783
 
10.0%
F7750
 
10.0%
m7750
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)77632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e23283
30.0%
a15533
20.0%
l15533
20.0%
M7783
 
10.0%
F7750
 
10.0%
m7750
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)77632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e23283
30.0%
a15533
20.0%
l15533
20.0%
M7783
 
10.0%
F7750
 
10.0%
m7750
 
10.0%

Age
Real number (ℝ)

Distinct1602
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.816308
Minimum14
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T09:47:16.147145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17.971786
Q120
median22.771612
Q326
95-th percentile35.322112
Maximum61
Range47
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.663167
Coefficient of variation (CV)0.23778526
Kurtosis3.5963108
Mean23.816308
Median Absolute Deviation (MAD)3.228388
Skewness1.5719763
Sum369938.71
Variance32.07146
MonotonicityNot monotonic
2025-10-23T09:47:16.328570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181438
 
9.3%
261337
 
8.6%
211252
 
8.1%
23900
 
5.8%
19674
 
4.3%
20395
 
2.5%
17394
 
2.5%
22381
 
2.5%
33156
 
1.0%
27130
 
0.8%
Other values (1592)8476
54.6%
ValueCountFrequency (%)
143
 
< 0.1%
152
 
< 0.1%
1675
0.5%
16.0932343
 
< 0.1%
16.1206991
 
< 0.1%
16.1292795
 
< 0.1%
16.1407511
 
< 0.1%
16.1729923
 
< 0.1%
16.1784831
 
< 0.1%
16.1981532
 
< 0.1%
ValueCountFrequency (%)
612
 
< 0.1%
561
 
< 0.1%
55.4936871
 
< 0.1%
55.2725731
 
< 0.1%
55.246251
 
< 0.1%
55.1378812
 
< 0.1%
55.0224948
 
0.1%
5527
0.2%
522
 
< 0.1%
511
 
< 0.1%

Height
Real number (ℝ)

High correlation 

Distinct1723
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6999182
Minimum1.45
Maximum1.975663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T09:47:16.532761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile1.556211
Q11.630927
median1.7
Q31.762921
95-th percentile1.8457554
Maximum1.975663
Range0.525663
Interquartile range (IQR)0.131994

Descriptive statistics

Standard deviation0.087670026
Coefficient of variation (CV)0.051573086
Kurtosis-0.56183456
Mean1.6999182
Median Absolute Deviation (MAD)0.066509
Skewness0.01000144
Sum26404.829
Variance0.0076860335
MonotonicityNot monotonic
2025-10-23T09:47:16.767225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.71018
 
6.6%
1.65570
 
3.7%
1.75504
 
3.2%
1.6491
 
3.2%
1.8394
 
2.5%
1.62303
 
2.0%
1.72230
 
1.5%
1.56190
 
1.2%
1.63186
 
1.2%
1.55164
 
1.1%
Other values (1713)11483
73.9%
ValueCountFrequency (%)
1.452
 
< 0.1%
1.4563462
 
< 0.1%
1.488
 
0.1%
1.4816821
 
< 0.1%
1.4832841
 
< 0.1%
1.4864843
 
< 0.1%
1.4894091
 
< 0.1%
1.4985613
 
< 0.1%
1.5131
0.8%
1.5026092
 
< 0.1%
ValueCountFrequency (%)
1.9756634
 
< 0.1%
1.9474063
 
< 0.1%
1.9427253
 
< 0.1%
1.9312632
 
< 0.1%
1.9312421
 
< 0.1%
1.9304161
 
< 0.1%
1.9311
0.1%
1.921
 
< 0.1%
1.9195572
 
< 0.1%
1.9195432
 
< 0.1%

Weight
Real number (ℝ)

High correlation 

Distinct1836
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.785225
Minimum39
Maximum165.05727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T09:47:16.982296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile49
Q166
median84
Q3111.60055
95-th percentile132.11649
Maximum165.05727
Range126.05727
Interquartile range (IQR)45.600553

Descriptive statistics

Standard deviation26.369144
Coefficient of variation (CV)0.30038249
Kurtosis-0.98657917
Mean87.785225
Median Absolute Deviation (MAD)22.735215
Skewness0.10855155
Sum1363567.9
Variance695.33177
MonotonicityNot monotonic
2025-10-23T09:47:17.201484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80644
 
4.1%
75494
 
3.2%
50450
 
2.9%
60377
 
2.4%
70376
 
2.4%
45242
 
1.6%
65229
 
1.5%
78225
 
1.4%
85223
 
1.4%
42213
 
1.4%
Other values (1826)12060
77.6%
ValueCountFrequency (%)
391
 
< 0.1%
39.1018053
< 0.1%
39.126311
 
< 0.1%
39.3715232
< 0.1%
39.5350471
 
< 0.1%
39.5811591
 
< 0.1%
39.6952953
< 0.1%
39.8501371
 
< 0.1%
402
< 0.1%
40.2027733
< 0.1%
ValueCountFrequency (%)
165.0572693
 
< 0.1%
160.93535111
0.1%
160.6394053
 
< 0.1%
155.8720932
 
< 0.1%
155.2426722
 
< 0.1%
154.6184463
 
< 0.1%
153.9599452
 
< 0.1%
153.1494916
< 0.1%
152.7205454
 
< 0.1%
152.5676715
< 0.1%

family_history_with_overweight
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
True
12696 
False
2837 
ValueCountFrequency (%)
True12696
81.7%
False2837
 
18.3%
2025-10-23T09:47:17.339328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

FAVC
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
True
14184 
False
 
1349
ValueCountFrequency (%)
True14184
91.3%
False1349
 
8.7%
2025-10-23T09:47:17.388864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

FCVC
Real number (ℝ)

Distinct872
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4429168
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T09:47:17.487766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.850496
Q12
median2.34222
Q33
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53089467
Coefficient of variation (CV)0.21732
Kurtosis-0.91646326
Mean2.4429168
Median Absolute Deviation (MAD)0.405741
Skewness-0.33204298
Sum37945.826
Variance0.28184915
MonotonicityNot monotonic
2025-10-23T09:47:17.650104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25816
37.4%
35669
36.5%
1199
 
1.3%
2.967392
 
0.6%
2.76661239
 
0.3%
2.93861637
 
0.2%
2.955331
 
0.2%
2.22514924
 
0.2%
2.5764923
 
0.1%
2.81993423
 
0.1%
Other values (862)3580
23.0%
ValueCountFrequency (%)
1199
1.3%
1.0025641
 
< 0.1%
1.0035662
 
< 0.1%
1.00557812
 
0.1%
1.0064361
 
< 0.1%
1.008765
 
< 0.1%
1.0211361
 
< 0.1%
1.03114911
 
0.1%
1.0361596
 
< 0.1%
1.0364144
 
< 0.1%
ValueCountFrequency (%)
35669
36.5%
2.9984412
 
< 0.1%
2.9979518
 
0.1%
2.9975244
 
< 0.1%
2.9967178
 
0.1%
2.9961865
 
< 0.1%
2.9955993
 
< 0.1%
2.994481
 
< 0.1%
2.9936341
 
< 0.1%
2.9926061
 
< 0.1%

NCP
Real number (ℝ)

Distinct645
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7604249
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T09:47:17.798879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q33
95-th percentile3.489918
Maximum4
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70646285
Coefficient of variation (CV)0.2559254
Kurtosis1.8251762
Mean2.7604249
Median Absolute Deviation (MAD)0
Skewness-1.5612644
Sum42877.68
Variance0.49908976
MonotonicityNot monotonic
2025-10-23T09:47:17.936034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310985
70.7%
11493
 
9.6%
4536
 
3.5%
2.99362330
 
0.2%
2.69539619
 
0.1%
1.89438418
 
0.1%
2.65883717
 
0.1%
2.99363416
 
0.1%
2.96200416
 
0.1%
2.97790916
 
0.1%
Other values (635)2387
 
15.4%
ValueCountFrequency (%)
11493
9.6%
1.0002833
 
< 0.1%
1.0004142
 
< 0.1%
1.000615
 
< 0.1%
1.0013832
 
< 0.1%
1.0015427
 
< 0.1%
1.0016337
 
< 0.1%
1.0094264
 
< 0.1%
1.0103196
 
< 0.1%
1.0149162
 
< 0.1%
ValueCountFrequency (%)
4536
3.5%
3.9987662
 
< 0.1%
3.9986184
 
< 0.1%
3.9959572
 
< 0.1%
3.9951473
 
< 0.1%
3.9945883
 
< 0.1%
3.9909252
 
< 0.1%
3.989554
 
< 0.1%
3.9894921
 
< 0.1%
3.9877077
 
< 0.1%

CAEC
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size879.3 KiB
Sometimes
13126 
Frequently
1858 
Always
 
346
no
 
203

Length

Max length10
Median length9
Mean length8.9613082
Min length2

Characters and Unicode

Total characters139196
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowFrequently
3rd rowSometimes
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes13126
84.5%
Frequently1858
 
12.0%
Always346
 
2.2%
no203
 
1.3%

Length

2025-10-23T09:47:18.067463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T09:47:18.152193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sometimes13126
84.5%
frequently1858
 
12.0%
always346
 
2.2%
no203
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e29968
21.5%
m26252
18.9%
t14984
10.8%
s13472
9.7%
o13329
9.6%
S13126
9.4%
i13126
9.4%
y2204
 
1.6%
l2204
 
1.6%
n2061
 
1.5%
Other values (7)8470
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)139196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e29968
21.5%
m26252
18.9%
t14984
10.8%
s13472
9.7%
o13329
9.6%
S13126
9.4%
i13126
9.4%
y2204
 
1.6%
l2204
 
1.6%
n2061
 
1.5%
Other values (7)8470
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)139196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e29968
21.5%
m26252
18.9%
t14984
10.8%
s13472
9.7%
o13329
9.6%
S13126
9.4%
i13126
9.4%
y2204
 
1.6%
l2204
 
1.6%
n2061
 
1.5%
Other values (7)8470
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)139196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e29968
21.5%
m26252
18.9%
t14984
10.8%
s13472
9.7%
o13329
9.6%
S13126
9.4%
i13126
9.4%
y2204
 
1.6%
l2204
 
1.6%
n2061
 
1.5%
Other values (7)8470
 
6.1%

SMOKE
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
False
15356 
True
 
177
ValueCountFrequency (%)
False15356
98.9%
True177
 
1.1%
2025-10-23T09:47:18.213736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

CH2O
Real number (ℝ)

Distinct1408
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0276261
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T09:47:18.307341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.796257
median2
Q32.531456
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)0.735199

Descriptive statistics

Standard deviation0.60773268
Coefficient of variation (CV)0.29972621
Kurtosis-0.73670285
Mean2.0276261
Median Absolute Deviation (MAD)0.39886
Skewness-0.20927674
Sum31495.116
Variance0.36933901
MonotonicityNot monotonic
2025-10-23T09:47:18.448866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24992
32.1%
12117
 
13.6%
31172
 
7.5%
2.86816750
 
0.3%
2.82562948
 
0.3%
2.61951745
 
0.3%
2.7200542
 
0.3%
2.62553740
 
0.3%
2.77073238
 
0.2%
2.61392833
 
0.2%
Other values (1398)6956
44.8%
ValueCountFrequency (%)
12117
13.6%
1.0004632
 
< 0.1%
1.0005364
 
< 0.1%
1.0005444
 
< 0.1%
1.0006952
 
< 0.1%
1.0012081
 
< 0.1%
1.0019951
 
< 0.1%
1.0022926
 
< 0.1%
1.0030632
 
< 0.1%
1.0035311
 
< 0.1%
ValueCountFrequency (%)
31172
7.5%
2.9994953
 
< 0.1%
2.996751
 
< 0.1%
2.9916712
 
< 0.1%
2.9893893
 
< 0.1%
2.9887712
 
< 0.1%
2.9877186
 
< 0.1%
2.9874062
 
< 0.1%
2.9843233
 
< 0.1%
2.9841536
 
< 0.1%

SCC
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
False
15019 
True
 
514
ValueCountFrequency (%)
False15019
96.7%
True514
 
3.3%
2025-10-23T09:47:18.541417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

FAF
Real number (ℝ)

Zeros 

Distinct1274
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9769677
Minimum0
Maximum3
Zeros3799
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T09:47:18.647057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00705
median1
Q31.582675
95-th percentile2.491946
Maximum3
Range3
Interquartile range (IQR)1.575625

Descriptive statistics

Standard deviation0.83684097
Coefficient of variation (CV)0.85656974
Kurtosis-0.47443387
Mean0.9769677
Median Absolute Deviation (MAD)0.869583
Skewness0.51452887
Sum15175.239
Variance0.7003028
MonotonicityNot monotonic
2025-10-23T09:47:18.785886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03799
24.5%
13103
20.0%
21737
 
11.2%
3603
 
3.9%
0.0158637
 
0.2%
1.09790535
 
0.2%
1.6824933
 
0.2%
1.42703733
 
0.2%
1.86683927
 
0.2%
0.92620124
 
0.2%
Other values (1264)6102
39.3%
ValueCountFrequency (%)
03799
24.5%
9.6 × 10-57
 
< 0.1%
0.0002728
 
0.1%
0.0004547
 
< 0.1%
0.0010158
 
0.1%
0.0010867
 
< 0.1%
0.0012725
 
< 0.1%
0.00129718
 
0.1%
0.002034
 
< 0.1%
0.003427
 
< 0.1%
ValueCountFrequency (%)
3603
3.9%
2.9999182
 
< 0.1%
2.9775431
 
< 0.1%
2.9718322
 
< 0.1%
2.9365514
 
< 0.1%
2.9315273
 
< 0.1%
2.89292217
 
0.1%
2.8919867
 
< 0.1%
2.891188
 
0.1%
2.8818381
 
< 0.1%

TUE
Real number (ℝ)

Zeros 

Distinct1207
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61381329
Minimum0
Maximum2
Zeros4966
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T09:47:18.922001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.566353
Q31
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.60222272
Coefficient of variation (CV)0.9811171
Kurtosis-0.41502667
Mean0.61381329
Median Absolute Deviation (MAD)0.460458
Skewness0.67442988
Sum9534.3619
Variance0.3626722
MonotonicityNot monotonic
2025-10-23T09:47:19.061537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04966
32.0%
13255
21.0%
2844
 
5.4%
0.002660
 
0.4%
0.72315446
 
0.3%
0.08823643
 
0.3%
0.63086637
 
0.2%
0.1517137
 
0.2%
0.6253531
 
0.2%
0.20037930
 
0.2%
Other values (1197)6184
39.8%
ValueCountFrequency (%)
04966
32.0%
7.3 × 10-52
 
< 0.1%
0.0003551
 
< 0.1%
0.0004363
 
< 0.1%
0.0010965
 
< 0.1%
0.0013310
 
0.1%
0.0013376
 
< 0.1%
0.001351
 
< 0.1%
0.0015189
 
0.1%
0.001599
 
0.1%
ValueCountFrequency (%)
2844
5.4%
1.992192
 
< 0.1%
1.9909251
 
< 0.1%
1.9906173
 
< 0.1%
1.9836781
 
< 0.1%
1.9808755
 
< 0.1%
1.9780437
 
< 0.1%
1.9729262
 
< 0.1%
1.9711710
 
0.1%
1.9695075
 
< 0.1%

CALC
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size854.1 KiB
Sometimes
11285 
no
3841 
Frequently
 
407

Length

Max length10
Median length9
Mean length7.2952424
Min length2

Characters and Unicode

Total characters113317
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowno
3rd rowno
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes11285
72.7%
no3841
 
24.7%
Frequently407
 
2.6%

Length

2025-10-23T09:47:19.195962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T09:47:19.274316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sometimes11285
72.7%
no3841
 
24.7%
frequently407
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e23384
20.6%
m22570
19.9%
o15126
13.3%
t11692
10.3%
S11285
10.0%
i11285
10.0%
s11285
10.0%
n4248
 
3.7%
F407
 
0.4%
r407
 
0.4%
Other values (4)1628
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)113317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e23384
20.6%
m22570
19.9%
o15126
13.3%
t11692
10.3%
S11285
10.0%
i11285
10.0%
s11285
10.0%
n4248
 
3.7%
F407
 
0.4%
r407
 
0.4%
Other values (4)1628
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)113317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e23384
20.6%
m22570
19.9%
o15126
13.3%
t11692
10.3%
S11285
10.0%
i11285
10.0%
s11285
10.0%
n4248
 
3.7%
F407
 
0.4%
r407
 
0.4%
Other values (4)1628
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)113317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e23384
20.6%
m22570
19.9%
o15126
13.3%
t11692
10.3%
S11285
10.0%
i11285
10.0%
s11285
10.0%
n4248
 
3.7%
F407
 
0.4%
r407
 
0.4%
Other values (4)1628
 
1.4%

MTRANS
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Public_Transportation
12470 
Automobile
2669 
Walking
 
340
Motorbike
 
30
Bike
 
24

Length

Max length21
Median length21
Mean length18.754008
Min length4

Characters and Unicode

Total characters291306
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPublic_Transportation
2nd rowAutomobile
3rd rowPublic_Transportation
4th rowPublic_Transportation
5th rowPublic_Transportation

Common Values

ValueCountFrequency (%)
Public_Transportation12470
80.3%
Automobile2669
 
17.2%
Walking340
 
2.2%
Motorbike30
 
0.2%
Bike24
 
0.2%

Length

2025-10-23T09:47:19.367762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T09:47:19.454245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
public_transportation12470
80.3%
automobile2669
 
17.2%
walking340
 
2.2%
motorbike30
 
0.2%
bike24
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o30338
10.4%
i28003
 
9.6%
t27639
 
9.5%
a25280
 
8.7%
n25280
 
8.7%
r24970
 
8.6%
l15479
 
5.3%
b15169
 
5.2%
u15139
 
5.2%
P12470
 
4.3%
Other values (13)71539
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)291306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o30338
10.4%
i28003
 
9.6%
t27639
 
9.5%
a25280
 
8.7%
n25280
 
8.7%
r24970
 
8.6%
l15479
 
5.3%
b15169
 
5.2%
u15139
 
5.2%
P12470
 
4.3%
Other values (13)71539
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)291306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o30338
10.4%
i28003
 
9.6%
t27639
 
9.5%
a25280
 
8.7%
n25280
 
8.7%
r24970
 
8.6%
l15479
 
5.3%
b15169
 
5.2%
u15139
 
5.2%
P12470
 
4.3%
Other values (13)71539
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)291306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o30338
10.4%
i28003
 
9.6%
t27639
 
9.5%
a25280
 
8.7%
n25280
 
8.7%
r24970
 
8.6%
l15479
 
5.3%
b15169
 
5.2%
u15139
 
5.2%
P12470
 
4.3%
Other values (13)71539
24.6%

WeightCategory
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size987.2 KiB
Obesity_Type_III
2983 
Obesity_Type_II
2403 
Normal_Weight
2345 
Obesity_Type_I
2207 
Overweight_Level_II
1881 
Other values (2)
3714 

Length

Max length19
Median length16
Mean length16.070109
Min length13

Characters and Unicode

Total characters249617
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOverweight_Level_II
2nd rowNormal_Weight
3rd rowInsufficient_Weight
4th rowObesity_Type_III
5th rowOverweight_Level_II

Common Values

ValueCountFrequency (%)
Obesity_Type_III2983
19.2%
Obesity_Type_II2403
15.5%
Normal_Weight2345
15.1%
Obesity_Type_I2207
14.2%
Overweight_Level_II1881
12.1%
Insufficient_Weight1870
12.0%
Overweight_Level_I1844
11.9%

Length

2025-10-23T09:47:19.560815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T09:47:19.673820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
obesity_type_iii2983
19.2%
obesity_type_ii2403
15.5%
normal_weight2345
15.1%
obesity_type_i2207
14.2%
overweight_level_ii1881
12.1%
insufficient_weight1870
12.0%
overweight_level_i1844
11.9%

Most occurring characters

ValueCountFrequency (%)
e36171
14.5%
_26851
 
10.8%
I23438
 
9.4%
i19273
 
7.7%
t17403
 
7.0%
y15186
 
6.1%
O11318
 
4.5%
s9463
 
3.8%
g7940
 
3.2%
h7940
 
3.2%
Other values (17)74634
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)249617
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e36171
14.5%
_26851
 
10.8%
I23438
 
9.4%
i19273
 
7.7%
t17403
 
7.0%
y15186
 
6.1%
O11318
 
4.5%
s9463
 
3.8%
g7940
 
3.2%
h7940
 
3.2%
Other values (17)74634
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)249617
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e36171
14.5%
_26851
 
10.8%
I23438
 
9.4%
i19273
 
7.7%
t17403
 
7.0%
y15186
 
6.1%
O11318
 
4.5%
s9463
 
3.8%
g7940
 
3.2%
h7940
 
3.2%
Other values (17)74634
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)249617
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e36171
14.5%
_26851
 
10.8%
I23438
 
9.4%
i19273
 
7.7%
t17403
 
7.0%
y15186
 
6.1%
O11318
 
4.5%
s9463
 
3.8%
g7940
 
3.2%
h7940
 
3.2%
Other values (17)74634
29.9%

Interactions

2025-10-23T09:47:13.355611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:04.166646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:05.599835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:06.573578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:07.792793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:08.846544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:09.939836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:11.205996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:12.317266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:13.486494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:04.558831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:05.715292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:06.696539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:07.909573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:08.971210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:10.061157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:11.334033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:12.435187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:13.594603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:04.736612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:05.812461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:06.804621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:08.015273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:09.083883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:10.180679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:11.444874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:12.542609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:13.719435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:04.860650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:05.911578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:06.910911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:08.141646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:09.214420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:10.286829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:11.559363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:12.653496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:13.836305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:04.994295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:06.028486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:07.029570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:08.258060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:09.337293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:10.403147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:11.682869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:12.769728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:13.960614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:05.119024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:06.137865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:07.327855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:08.386521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:09.453486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:10.524629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:11.804187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:12.890800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:14.076487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:05.235858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:06.243549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:07.438494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:08.497333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:09.572167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:10.849196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:11.922160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:13.003212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:14.198379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:05.359211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:06.355909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:07.560929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:08.612929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:09.701409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:10.966487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:12.044080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:13.116276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:14.337124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:05.477829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:06.464418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:07.671682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:08.728788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:09.816920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:11.079147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:12.164329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T09:47:13.223581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-23T09:47:19.823227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeCAECCALCCH2OFAFFAVCFCVCGenderHeightMTRANSNCPSCCSMOKETUEWeightWeightCategoryfamily_history_with_overweightid
Age1.0000.1540.1920.086-0.2740.1220.0960.2540.0160.363-0.1210.1150.142-0.3020.4380.3470.3010.007
CAEC0.1541.0000.0950.1530.1160.1380.0920.0720.1400.0650.1510.1270.0160.1220.3120.3310.3380.000
CALC0.1920.0951.0000.1800.1470.1150.1630.0800.1250.0710.1340.0000.0140.1630.2920.3090.0130.000
CH2O0.0860.1530.1801.0000.0590.1660.1100.3310.1890.1010.0770.0770.051-0.0040.3450.3100.2770.008
FAF-0.2740.1160.1470.0591.0000.135-0.0860.3440.3210.1010.1180.0790.042-0.017-0.0620.2570.1830.017
FAVC0.1220.1380.1150.1660.1351.0000.0940.0200.1520.1240.0520.1120.0130.1380.2370.2740.1510.016
FCVC0.0960.0920.1630.110-0.0860.0941.0000.402-0.1080.0970.1290.0410.049-0.1340.2270.3250.132-0.009
Gender0.2540.0720.0800.3310.3440.0200.4021.0000.6380.1600.1570.0580.0570.2120.5230.6160.0930.000
Height0.0160.1400.1250.1890.3210.152-0.1080.6381.0000.0840.1120.1470.1080.0860.4240.2670.2980.013
MTRANS0.3630.0650.0710.1010.1010.1240.0970.1600.0841.0000.0530.0580.0300.1270.1530.1630.1300.000
NCP-0.1210.1510.1340.0770.1180.0520.1290.1570.1120.0531.0000.0700.0060.128-0.0220.2180.226-0.005
SCC0.1150.1270.0000.0770.0790.1120.0410.0580.1470.0580.0701.0000.0170.0670.2060.2170.1620.000
SMOKE0.1420.0160.0140.0510.0420.0130.0490.0570.1080.0300.0060.0171.0000.0310.0730.1000.0170.017
TUE-0.3020.1220.163-0.004-0.0170.138-0.1340.2120.0860.1270.1280.0670.0311.000-0.0620.2480.2010.001
Weight0.4380.3120.2920.345-0.0620.2370.2270.5230.4240.153-0.0220.2060.073-0.0621.0000.6470.5890.014
WeightCategory0.3470.3310.3090.3100.2570.2740.3250.6160.2670.1630.2180.2170.1000.2480.6471.0000.5590.008
family_history_with_overweight0.3010.3380.0130.2770.1830.1510.1320.0930.2980.1300.2260.1620.0170.2010.5890.5591.0000.000
id0.0070.0000.0000.0080.0170.016-0.0090.0000.0130.000-0.0050.0000.0170.0010.0140.0080.0001.000

Missing values

2025-10-23T09:47:14.530290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-23T09:47:14.734268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idGenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSWeightCategory
00Male24.4430111.69999881.669950yesyes2.0000002.983297Sometimesno2.763573no0.0000000.976473SometimesPublic_TransportationOverweight_Level_II
11Female18.0000001.56000057.000000yesyes2.0000003.000000Frequentlyno2.000000no1.0000001.000000noAutomobileNormal_Weight
22Female18.0000001.71146050.165754yesyes1.8805341.411685Sometimesno1.910378no0.8660451.673584noPublic_TransportationInsufficient_Weight
33Female20.9527371.710730131.274851yesyes3.0000003.000000Sometimesno1.674061no1.4678630.780199SometimesPublic_TransportationObesity_Type_III
44Male31.6410811.91418693.798055yesyes2.6796641.971472Sometimesno1.979848no1.9679730.931721SometimesPublic_TransportationOverweight_Level_II
55Male18.1282491.74852451.552595yesyes2.9197513.000000Sometimesno2.137550no1.9300331.000000SometimesPublic_TransportationInsufficient_Weight
66Male29.8830211.754711112.725005yesyes1.9912403.000000Sometimesno2.000000no0.0000000.696948SometimesAutomobileObesity_Type_II
77Male29.8914731.750150118.206565yesyes1.3974683.000000Sometimesno2.000000no0.5986550.000000SometimesAutomobileObesity_Type_II
88Male17.0000001.70000070.000000noyes2.0000003.000000Sometimesno3.000000yes1.0000001.000000noPublic_TransportationOverweight_Level_I
99Female26.0000001.638836111.275646yesyes3.0000003.000000Sometimesno2.632253no0.0000000.218645SometimesPublic_TransportationObesity_Type_III
idGenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSWeightCategory
1552315523Male19.0000001.80000085.000000yesno3.0000003.000000Sometimesno3.000000no3.0000000.000000SometimesWalkingOverweight_Level_I
1552415524Male24.0000001.70000062.000000yesno2.0000003.000000Frequentlyno2.000000no0.0000001.000000SometimesPublic_TransportationNormal_Weight
1552515525Female21.0000001.56000050.000000noyes2.0000003.000000Sometimesno1.000000no0.0000001.000000noPublic_TransportationNormal_Weight
1552615526Male21.0000001.72000066.000000noyes2.0000003.000000Sometimesno2.000000no1.0000001.000000noPublic_TransportationNormal_Weight
1552715527Female19.9112461.53264342.000000noyes2.7464083.994588Frequentlyno1.000000no2.0000000.000000SometimesPublic_TransportationInsufficient_Weight
1552815528Male18.0000001.70000050.000000noyes2.0000003.000000Frequentlyno2.000000no1.0000002.000000SometimesPublic_TransportationInsufficient_Weight
1552915529Male18.0000001.76310155.523481yesyes2.7860083.000000Sometimesno1.962646yes0.0282021.561272SometimesPublic_TransportationInsufficient_Weight
1553015530Female19.0102111.68693649.660995noyes1.0535343.452590Sometimesno1.000000no2.0012301.000000SometimesPublic_TransportationInsufficient_Weight
1553115531Male22.7778901.80544585.228116yesyes2.0000002.092179Sometimesno2.452986no0.7967700.000000SometimesPublic_TransportationOverweight_Level_I
1553215532Male39.3715231.77027879.677930yesyes2.4078171.097312Sometimesno2.205911no0.9779290.000000FrequentlyAutomobileOverweight_Level_II